Data Engineer

Data Engineer (Cloud & AI Platforms)

Moative Location: Chennai | Bangalore | Hyderabad | Pune | Mumbai | Remote

About Company Moative is an applied AI company and AI venture lab that builds transformation solutions for data-intensive industries including energy, utilities, healthcare, life sciences, and vertical SaaS. The team combines expertise across data science, machine learning, engineering, mathematics, and product development to create production-grade AI, analytics, and data platforms for global clients. We are looking for a highly motivated

Senior Data Engineer to build scalable data infrastructure that powers advanced analytics, machine learning, and AI-driven products across multiple industries.

Role Overview As a Senior Data Engineer, you will own the design, development, and optimization of modern cloud-based data platforms and pipelines. You will work closely with data scientists, ML engineers, and business stakeholders to ensure reliable, scalable, and high-quality data systems that support production AI and analytics workloads. This role is ideal for engineers who enjoy building robust data infrastructure, solving complex data challenges, and working in fast-paced environments where ownership and technical excellence matter.

Key Responsibilities Data Pipeline Engineering

• Design, build, and maintain scalable batch and real-time data pipelines across cloud platforms.
• Develop reliable ETL and ELT workflows to ingest, transform, and deliver data efficiently.
• Optimize pipeline performance, reliability, and cost across production environments.
• Ensure data availability and consistency for analytics and AI applications.

Cloud Data Platform Development

• Build and manage cloud-native data solutions using Azure, AWS, or GCP.
• Leverage modern storage, compute, orchestration, and integration services to support large-scale workloads.
• Implement scalable architectures capable of handling structured and semi-structured data.
• Support multi-cloud environments where required by client engagements.

Data Warehousing & Modeling

• Design and maintain data models that support reporting, analytics, and machine learning use cases.
• Work with data warehouses and lakehouse platforms such as Snowflake, Redshift, Synapse, or BigQuery.
• Improve data accessibility, performance, and governance across enterprise datasets.
• Collaborate with stakeholders to translate business requirements into scalable data solutions.

Data Quality & Observability

• Implement monitoring, alerting, and observability frameworks for critical pipelines.
• Establish automated data-quality checks, validation processes, and incident-management workflows.
• Troubleshoot production issues and drive root-cause analysis to improve platform reliability.
• Ensure high standards of data accuracy, consistency, and operational excellence.

AI & Analytics Enablement

• Partner with Data Scientists and ML Engineers to provide trusted datasets for model training and inference.
• Support analytics initiatives through efficient data processing and delivery mechanisms.
• Build infrastructure that enables experimentation, model deployment, and business intelligence use cases.
• Contribute to AI and data-product development initiatives across client engagements.

Governance & Best Practices

• Implement data-governance standards including lineage, cataloging, access controls, and auditability.
• Support compliance requirements across regulated industries and enterprise environments.
• Drive adoption of engineering best practices including CI/CD, testing, documentation, and automation.
• Promote scalable and maintainable architecture patterns across data platforms.

Ideal Candidate Profile

• 3–5 years of hands-on experience building and operating production-grade data pipelines and platforms.
• Strong proficiency in SQL and Python, with experience in PySpark or distributed data-processing frameworks.
• Experience designing ETL/ELT workflows and managing end-to-end data engineering lifecycle activities.
• Hands-on expertise with at least one major cloud platform (AWS, Azure, or GCP) and working knowledge of multiple cloud ecosystems.
• Strong understanding of data warehousing, data modeling, orchestration, and pipeline reliability principles.
• Experience with tools such as Airflow, dbt, Prefect, Dagster, Spark, Kafka, or equivalent technologies.
• Ability to work independently, communicate effectively, and drive projects from concept through delivery.
• Strong problem-solving mindset with the ability to navigate ambiguity and take ownership.

Preferred Qualifications

• Experience working in analytics, AI, machine learning, or data-product environments.
• Exposure to Snowflake, Redshift, BigQuery, Synapse, Databricks, or modern lakehouse architectures.
• Familiarity with Docker, Kubernetes, CI/CD pipelines, and infrastructure automation.
• Experience implementing data-governance, security, and compliance frameworks.
• Background in high-scale analytics organizations or data-centric product companies.

What We Offer

• Opportunity to build production data platforms powering real-world AI and analytics solutions.
• Exposure to cutting-edge machine-learning and data-engineering use cases across multiple industries.
• High-ownership environment where engineers contribute directly to architecture and business outcomes.
• Collaborative team of data scientists, ML engineers, AI specialists, and product builders.
• Hybrid work model with significant opportunities for learning, growth, and technical leadership.

Hiring Process Flexiple Interview → Profile Review → HR Screening Round → Moative Technical Interview → Moative Ownership & Communication Round → Final Alignment Round

Applications are being reviewed this week. Apply today to be included in the first round of interviews. We are shortlisting candidates within 24–48 hours — apply now to be considered.

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